Spatial CUSUM for Signal Region Detection
Xin Zhang, Zhengyuan Zhu
TL;DR
The paper introduces Spatial CUSUM (SCUSUM), a novel method for detecting weak clustered signals in spatial data, demonstrating high accuracy and sensitivity, especially in medical imaging applications.
Contribution
The paper develops the SCUSUM method combining CUSUM and false discovery rate control, with theoretical properties and superior detection of weak signals compared to existing methods.
Findings
SCUSUM achieves high classification accuracy asymptotically.
SCUSUM is sensitive to weak spatial signals in simulations.
SCUSUM detects more irregular weak signals in fMRI data than existing methods.
Abstract
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals with spatial structures. However, most of the existing methods are either too conservative for weak signals or computationally too intensive. In this paper, we consider a novel method named Spatial CUSUM (SCUSUM), which employs the idea of the CUSUM procedure and false discovery rate controlling. We develop theoretical properties of the method which indicates that asymptotically SCUSUM can reach high classification accuracy. In the simulation study, we demonstrate that SCUSUM is sensitive to weak spatial signals. This new method is applied to a real fMRI dataset as…
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Taxonomy
TopicsData-Driven Disease Surveillance · Statistical Methods and Inference · Bayesian Methods and Mixture Models